Object detection is one of the fundamental tasks in computer vision, aiming to localize the objects observed in an image and classify them. Recently, much progress has been made via the development of both one-stage [1]}, [2]}, [3]}, [4]}, [5]} and two-stage [6]}, [7]}, [8]}, [9]} deep object detection frameworks, significantly improving the mean average precision (mAP) on standard benchmarks [10]}, [11]}, [12]}. However, the performance of these models typically increases with their size, and so does their inference runtime. This conflicts with their deployment on embedded platforms, such as mobile phones, drones, and autonomous vehicles, which involve computation and memory constraints. While some efforts have been made to design smaller detectors, such as SSD [2]}, YOLO [1]} and detectors with lightweight backbones [15]}, the performance of these methods does not match that of deeper ones.
